Artificial Intelligence, Machine Learning and Society
Annual Reviews has curated a collection of free research articles that explore the impact of artificial intelligence and machine learning on modern life and society.
Annual Reviews is a nonprofit publisher dedicated to synthesizing and integrating knowledge for the advancement of science and the benefit of society. Among its goals is the publication of research articles with the aim of stimulating discussion of science. Specifically these items:
- Capture the current understanding of a topic, including what is well supported and what is controversial
- Place the work in its historical context
- Highlight the major questions that remain to be answered and the probable evolution of research in the years to come
- Describe the practical applications and general importance of research for society.
The topics of AI and Artificial Intelligence and Machine Learning are the ones that interest I Programmer and that we address whenever the opportunity arises. Among our recent AI coverage, we reported on AI Ethics, a free text-based online course by the University of Helsinki for anyone interested in the ethical aspects of AI :
Ethics concerns questions of how developers, manufacturers, authorities and operators should behave in order to minimize the ethical risks that can arise from AI in society, whether in design, improper application or intentional misuse of technology.
Also with ethics, in 2019 we looked at all of the current guidelines from the European Commission on how to build AIs that society can trust:
The objective of these guidelines is to promote what is known as “trustworthy AI”, comprising the following three components:
It must be legal, compliant with all applicable laws and regulations
It must be ethical, guaranteeing respect for ethical principles and values
It must be robust, both technically and socially because, even with good intentions, AI systems can cause unintended harm.
The Article Reviews’ recently published Special Article Collection Archive on AI, machine learning and societycontains articles that elaborate on the topic of ethics, but also go beyond it by looking at other applications of AI, and specifically in four key areas:
- Social implications of artificial intelligence
- Law enforcement and human rights
- Medical applications of Big Data
- Autonomous systems and robotics
The collection is made up of 23 journal articles, taken from 13 annual journal reviews. Grouped by theme, these are:
Social implications of artificial intelligence
- Artificial Intelligence in Action: Confronting the COVID-19 Pandemic with Natural Language Processing
- Machine learning for the social sciences: an agnostic approach
- Deep Learning Syntactic Structure
- The Algorithm Society
- Big Data in Industrial and Organizational Psychology and Human Resource Management: Advancing Organizational Research and Practice
- The Challenge of Big Data and Data Science
- Machine Learning Methods Economists Should Know
Law enforcement and human rights
- Artificial Intelligence, Predictive Policing and Risk Assessment for Law Enforcement
- Monitoring tool or spotlight on inequalities? Big Data and the law
- Human rights and technology: new challenges for justice and accountability
- Are emerging military technologies important for international politics?
Medical applications of Big Data
- Ethical Machine Learning in Healthcare
- Modern Clinical Text Mining: Guide and Review
- AI in measurement science
- Artificial intelligence in addiction treatment
- Big Data and Artificial Intelligence Modeling for Drug Discovery
- Large-scale analysis of genetic and clinical patient data
- Deep Neural Networks: A New Framework for Modeling Biological Vision and Brain Information Processing
Autonomous systems and robotics
- Autonomy in surgical robotics
- Autonomous vehicles and public health
- Learning-based predictive control: towards safe learning in control
- Data-Driven Predictive Control for Autonomous Systems
- Autonomy in rehabilitation robotics: an intersection
All articles are based on solid research and are very interesting. However, I have picked out a few that might be considered candidates to read first.
I would rank first, “Artificial Intelligence in Action: Coping with the COVID-19 Pandemic with Natural Language Processing” which is a topical and timely topic. The research focuses on how NLP can be applied to meet many of the information needs made urgent by the COVID-19 pandemic,
directly address aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, caseload forecasting, and misinformation detection.
Without throwing COVID-19 into the mix, NLP itself is a hot topic. For helpful resources, see two Iprogrammer articles “Take Stanford’s Natural Language Understanding For Free” and “Take Stanford’s Natural Language Processing with Deep Learning For Free”.
Next in my reading list would be “The Challenge of Big Data and Data Science” :
Big data and data science are transforming the world in ways that bring new concerns to social scientists, such as the impacts of the Internet on citizens and the media, the repercussions of smart cities, the possibilities of cyber warfare and cyberterrorism, the implications of precision medicine, and the consequences of artificial intelligence and automation.
Then, on the ethics front, “Human Rights and Technology: New Challenges for Justice and Accountability”which
examines contemporary challenges in the field of technology and human rights. The increased use of artificial intelligence (AI) in decision-making in the public and private sectors – for example, in criminal justice, employment, civil service and financial contexts – poses a significant threat to human rights.
This is an important topic that is already on the minds of society given the challenges and questions raised by the use of ClearView AI and the collection of biometrics in a context of oppression and invasion of privacy. Check out my 2016 article “OpenFace – Facial Recognition for All” for some of the challenges, long before they hit trending status.
Facial recognition, once reserved for the few, such as intelligence and security services, is now also accessible to as many people as possible, thanks to OpenFace.
The law, unprepared for the challenges that such technology heralds, cannot keep up with technological advancements because it has no answers to any of the aforementioned dilemmas.
One thing is certain, however – with this technology comes great power, and with great power comes great responsibility. As the authors themselves say:
“Please use responsibly!
We do not support the use of this project in applications that violate privacy and security. We use it to help users with cognitive impairments feel and understand the world around them.”
Last but not least, I will tackle the next “Big Data and Artificial Intelligence Modeling for Drug Discovery”
Due to the massive datasets available for drug candidates, modern drug discovery has moved into the era of big data. At the heart of this change is the development of artificial intelligence approaches to implement innovative modeling based on the dynamic, heterogeneous and large nature of drug datasets.
In an attempt to identify what triggered the disease, AI predicts how a virus strain will evolve and target it, as well as identifying individual specific treatments.
It will take a few hours to read all the articles but it is worth it as it will help you understand how society will benefit from the application of AI. At the same time, you will learn what kind of multi-aspect challenges it will entail.
Article Reviews Special Article Collection Archive
AI Ethics – A Course from Finland
Ethical Guidelines for Trustworthy AI
Get Free Natural Language Understanding from Stanford
Get Free Stanford Natural Language Processing with Deep Learning
OpenFace – Face recognition for everyone
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